Taylor-AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech

摘要

Communication becomes effective when the speech signal arrives with the profound characteristics. This insisted the researchers to develop an automatic system of recognizing the speech signals from the murmurs. Some of the traditional automatic recognition systems are unfit for the silent environments imposing a need for an effective recognition system. Also, the traditional automatic recognition methods, like Neural Networks, render poor performance in the presence of the murmurs. Thus, this article proposes a method for automatic whisper recognition using the Deep Convolutional Neural Network (DCNN). The training of the DCNN is performed using the proposed Stochastic-Whale Optimization Algorithm (Stochastic-WOA), which is designed by the integration of Stochastic Gradient Descent algorithm with WOA. The input to the classifier is the features that include pitch chroma, spectral centroid, spectral skewness, and Taylor-Amplitude Modulation Spectrogram (Taylor-AMS), which is obtained by combining Taylor series and Amplitude Modulation Spectrogram (AMS) features, of the preprocessed input speech signal. The experimentation of the method is performed using the real database and the analysis proves that the proposed method acquired a maximal accuracy of 0.9723, minimal False Positive Rate of 0.0257, and maximal True Positive Rate of 0.9981, respectively.